Distribution network state estimation method and device based on trust region

Abstract

The invention relates to the technical field of power distribution networks, in particular to a distribution network state estimation method and device based on a trust region, and the method comprises the steps: obtaining the upper and lower limits of active power output and the upper and lower limits of reactive power output of a photovoltaic power generation system in a power distribution network of a power system; determining an inequality constraint condition according to the active output upper and lower limits and the reactive output upper and lower limits, and establishing a power distribution network state estimation model based on the inequality constraint condition; and performing optimization solution on the power distribution network state estimation model by using a preset trust region algorithm to obtain a target solution, and determining a power distribution network state estimation result of the power system by using the target solution. Therefore, the problems of long iteration time, large occupied memory, poor applicability, low applicability and high efficiency of state estimation of the power system and the like of an algorithm for state estimation of the power system in related technologies are solved.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network, and in particular to a distribution network state estimation method and device based on trust region. Background Art

[0002] Power system state estimation is one of the core basic modules in the energy management system. Its purpose is to obtain accurate state information of each node and line in the system through real-time monitoring and estimation of the power system, including voltage amplitude, phase angle, active power and reactive power, etc. This state information is of great significance to the safe and stable operation of the power system and energy management.

[0003] In the related technology, since the basis of power system state estimation is the mathematical model and measurement data of the power system, the model of the power system is a complex nonlinear system. The state estimation algorithm of the power system can use mathematical methods, such as least squares method, maximum likelihood estimation, etc., to linearize and optimize the model of the power system to obtain the best estimation result of the power system state. The state estimation result can be provided to other energy management system modules, such as power market operation, fault diagnosis and safety control, etc., for power system operation monitoring, fault detection, load forecasting, optimal scheduling and other functions, which plays an important role in realizing the intelligent operation and optimized management of the power system.

[0004] Among them, the basis of power system state estimation is the mathematical model and measurement data of the power system. In the power system state estimation result, the injected power of the zero injection node should be strictly 0. Otherwise, since there is no generator or load associated with the zero injection node, the state estimation results at these nodes will not satisfy the power flow equation. Now the solution methods are divided into numerical solutions, including: reduced Newton method, modified Newton method, and modified fast decoupling method. The reduced Newton method is applicable to rectangular coordinates, the modified fast decoupling estimation method is applicable to polar coordinates, and the modified Newton method is applicable to both polar and rectangular coordinates. In addition, there are methods for calculation using models and data-driven methods.

[0005] However, in the related technologies, both the reduced Newton method and the modified fast decoupling estimation method are only applicable to one coordinate system, while the modified Newton method is applicable to two coordinate systems. However, the iteration time is long and the memory usage is large. In addition, both are unconstrained optimization problems, resulting in poor applicability in distribution networks with a large number of photovoltaic power generation connections, which reduces the applicability of power system state estimation. In addition, the method of combining model and data-driven calculation takes a long time in data-driven calculation, which reduces the efficiency of power system state estimation. Summary of the invention

[0006] The present application provides a distribution network state estimation method and device based on trust region to solve the problems in the related art such as long algorithm iteration time, large memory usage, poor applicability, and reduced applicability and efficiency of power system state estimation.

[0007] The first aspect of the present application provides a distribution network state estimation method based on a trust region, comprising the following steps: obtaining the upper and lower limits of active output and the upper and lower limits of reactive output of a photovoltaic power generation system in a distribution network of the power system; determining inequality constraints according to the upper and lower limits of active output and the upper and lower limits of reactive output, and establishing a distribution network state estimation model based on the inequality constraints; optimizing and solving the distribution network state estimation model using a preset trust region algorithm to obtain a target solution, so as to use the target solution to determine a distribution network state estimation result of the power system.

[0008] Optionally, in one embodiment of the present application, the method of optimizing and solving the distribution network state estimation model using a preset trust region algorithm to obtain a target solution includes: determining the optimization problem of the distribution network state estimation model, and preprocessing the distribution network state estimation model to obtain a state estimation model that meets preset processing conditions; based on the state estimation model, converting the optimization problem into a sub-problem of quadratic programming, and solving the sub-problem within a trust region radius to obtain a solution result, and detecting whether the solution result meets a preset no-solution condition; when it is detected that the solution result meets the preset no-solution condition, performing feasibility recovery on the solution result to obtain a target solution result, and judging whether the target solution result meets a preset convergence condition; if the target solution result meets the preset convergence condition, updating the target variable value of the state estimation model, and using the target variable value to obtain the target solution, otherwise adjusting the trust region radius until the target solution result meets the preset convergence condition.

[0009] Optionally, in one embodiment of the present application, performing feasibility recovery on the solution result to obtain the target solution result includes: based on a preset relaxation method, relaxing the constraints in the optimization problem to obtain the target solution result.

[0010] Optionally, in one embodiment of the present application, the expression of the preset relaxation method is:

[0011]

[0012] Where d represents the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; c(x (k) ) represents the value of the equation constraint in the kth iteration; x (k) Represents the value of the independent variable at the kth iteration.

[0013] Optionally, in one embodiment of the present application, the adjusting the trust region radius until the target solution result satisfies the preset convergence condition includes: reducing the trust region radius to a target radius that satisfies the preset convergence condition, and adding a target constraint that satisfies the preset convergence condition to the sub-problem to convert the sub-problem into a target sub-problem; based on the target sub-problem, obtaining a new target solution result so that the new target solution result satisfies the preset convergence condition.

[0014] Optionally, in one embodiment of the present application, the expression of the preset convergence condition is:

[0015] Δmerit ≥ σΔm

[0016] Among them, σ represents a coefficient used to measure the degree of decline, Δmerit represents the actual reduction value, and Δm represents the predicted decline value.

[0017] Optionally, in one embodiment of the present application, the expression of the target sub-problem is:

[0018]

[0019] Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, and the superscript k indicates the kth iteration; λ (k) represents the Lagrange multiplier in the kth iteration.

[0020] Optionally, in one embodiment of the present application, the upper and lower limits of active output and reactive output of the photovoltaic power generation system are:

[0021]

[0022] Among them, P DG and Q DG Represents the actual active and reactive power output of photovoltaic, P DG,min and P DG,max Indicates the lower and upper limits of photovoltaic active output, Q DG,min and Q DG,max Indicates the lower and upper limits of PV reactive output.

[0023] Optionally, in one embodiment of the present application, the distribution network state estimation model is:

[0024]

[0025] Among them, z i 、h i and w iare the measured values, measurement functions and weights of the voltage amplitude and phase angle of measurement i, respectively. are all system state variables, F is the nonlinear mapping matrix, u is the independent variable voltage amplitude, and θ is the independent variable voltage phase angle.

[0026] Optionally, in one embodiment of the present application, the expression of the preset trust region algorithm is:

[0027]

[0028] Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, the superscript k indicates the kth iteration; d indicates the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; λ (k) represents the Lagrange multiplier in the kth iteration; c(x (k) ) represents the value of the equation constraint at the kth iteration, where Represents the Hessian matrix.

[0029] The second aspect of the present application provides a distribution network state estimation device based on a trust region, including: an acquisition module, used to obtain the upper and lower limits of active output and the upper and lower limits of reactive output of a photovoltaic power generation system in the distribution network of the power system; an establishment module, used to determine inequality constraints according to the upper and lower limits of active output and the upper and lower limits of reactive output, and establish a distribution network state estimation model based on the inequality constraints; a determination module, used to optimize and solve the distribution network state estimation model using a preset trust region algorithm to obtain a target solution, so as to use the target solution to determine the distribution network state estimation result of the power system.

[0030] Optionally, in one embodiment of the present application, the determination module includes: a determination unit, used to determine the optimization problem of the distribution network state estimation model, and preprocess the distribution network state estimation model to obtain a state estimation model that meets preset processing conditions; a detection unit, used to convert the optimization problem into a sub-problem of quadratic programming based on the state estimation model, and solve the sub-problem within the trust region radius to obtain a solution result, and detect whether the solution result meets a preset no-solution condition; a judgment unit, used to perform feasibility recovery on the solution result when it is detected that the solution result meets the preset no-solution condition, to obtain a target solution result, and to judge whether the target solution result meets a preset convergence condition; a processing unit, used to update the target variable value of the state estimation model if the target solution result meets the preset convergence condition, and use the target variable value to obtain the target solution, otherwise adjust the trust region radius until the target solution result meets the preset convergence condition.

[0031] Optionally, in one embodiment of the present application, the judgment unit is further used to relax the constraints in the optimization problem based on a preset relaxation method to obtain the target solution result.

[0032] Optionally, in one embodiment of the present application, the expression of the preset relaxation method is:

[0033]

[0034] Where d represents the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; c(x (k) ) represents the value of the equation constraint in the kth iteration; x (k) Represents the value of the independent variable at the kth iteration.

[0035] Optionally, in one embodiment of the present application, the processing unit includes: a processing subunit, used to reduce the trust region radius to a target radius that satisfies a preset convergence condition, and add a target constraint that satisfies the preset convergence condition in the subproblem to convert the subproblem into a target subproblem; a determination subunit, used to obtain a new target solution result based on the target subproblem, so that the new target solution result satisfies the preset convergence condition.

[0036] Optionally, in one embodiment of the present application, the expression of the preset convergence condition is:

[0037] Δmerit ≥ σΔm

[0038] Among them, σ represents a coefficient used to measure the degree of decline, Δmerit represents the actual reduction value, and Δm represents the predicted decline value.

[0039] Optionally, in one embodiment of the present application, the expression of the target sub-problem is:

[0040]

[0041] Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, and the superscript k indicates the kth iteration; λ (k) represents the Lagrange multiplier in the kth iteration.

[0042] Optionally, in one embodiment of the present application, the upper and lower limits of active output and reactive output of the photovoltaic power generation system are:

[0043]

[0044] Among them, P DG and QDG Represents the actual active and reactive power output of photovoltaic, P DG,min and P DG,max Indicates the lower and upper limits of photovoltaic active output, Q DG,min and Q DG,max Indicates the lower and upper limits of PV reactive output.

[0045] Optionally, in one embodiment of the present application, the distribution network state estimation model is:

[0046]

[0047] Among them, z i 、h i and w i are the measured values, measurement functions and weights of the voltage amplitude and phase angle of measurement i, respectively. are all system state variables, F is the nonlinear mapping matrix, u is the independent variable voltage amplitude, and θ is the independent variable voltage phase angle.

[0048] Optionally, in one embodiment of the present application, the expression of the preset trust region algorithm is:

[0049]

[0050] Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, the superscript k indicates the kth iteration; d indicates the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; λ (k) represents the Lagrange multiplier in the kth iteration; c(x (k) ) represents the value of the equation constraint at the kth iteration, where Represents the Hessian matrix.

[0051] The embodiment of the present application can determine the inequality constraint conditions according to the upper and lower limits of the active output and the upper and lower limits of the reactive output of the photovoltaic power generation system in the distribution network of the power system, and based on the inequality constraint conditions, establish a distribution network state estimation model, use the trust region algorithm to optimize and solve the distribution network state estimation model, obtain the target solution, and use the target solution to determine the distribution network state estimation result of the power system, effectively improving the applicability and efficiency of the power system state estimation. Therefore, the problems of long algorithm iteration time, large memory usage, poor applicability, and reduced applicability and efficiency of power system state estimation in the related technology are solved.

[0052] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0054] Figure 1 A flowchart of a distribution network state estimation method based on a trust region provided according to an embodiment of the present application;

[0055] Figure 2 This is a flowchart of an optimization solution method based on a trust region algorithm according to a specific embodiment of the present application;

[0056] Figure 3 It is a structural schematic diagram of a distribution network state estimation device based on a trust region provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0057] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0058] The following describes the distribution network state estimation method and device based on the trust region of the embodiment of the present application with reference to the accompanying drawings. In view of the problem that the algorithm iteration time in the related technology mentioned in the background technology center is long, the memory is large, and the applicability is poor, which reduces the applicability and efficiency of the power system state estimation, the present application provides a distribution network state estimation method based on the trust region, in which the inequality constraint conditions can be determined according to the upper and lower limits of the active output and the upper and lower limits of the reactive output of the photovoltaic power generation system in the distribution network of the power system, and based on the inequality constraint conditions, a distribution network state estimation model is established, and the distribution network state estimation model is optimized and solved using the trust region algorithm to obtain the target solution, so as to use the target solution to determine the distribution network state estimation result of the power system, effectively improving the applicability and efficiency of the power system state estimation. As a result, the problem that the algorithm iteration time in the related technology is long, the memory is large, and the applicability is poor, which reduces the applicability and efficiency of the power system state estimation is solved.

[0059] Specifically, Figure 1 A flow chart of a method for estimating a distribution network state based on a trust region provided in an embodiment of the present application.

[0060] like Figure 1 As shown, the distribution network state estimation method based on the trust region includes the following steps:

[0061] In step S101, the upper and lower limits of active output and reactive output of a photovoltaic power generation system in a power distribution network of the power system are obtained.

[0062] It can be understood that the embodiments of the present application can obtain the upper and lower limits of active output and reactive output of the photovoltaic power generation system in the distribution network of the power system. For example, the embodiments of the present application can be combined with real-time meteorological data, photovoltaic equipment performance parameters, historical operation records and power grid dispatching requirements, etc., through data analysis and prediction, so that the distribution network operator and the dispatching center can accurately grasp the real-time and expected active and reactive output boundaries of the photovoltaic power generation system, and provide reliable data support for distribution network status estimation and dispatching decisions.

[0063] For example, in the embodiment of the present application, the zero injection node may be first considered in the modeling of the power system state estimation, and the power system network matrix in complex form may be expressed as:

[0064]

[0065] Where Y is the system admittance matrix, The complex voltages of the zero injection node and the non-zero injection node are respectively, The complex current is injected into the non-zero injection node, and the complex current of the zero injection node is 0; the subscript B represents the electrical quantity related to the zero injection node, and the subscript N represents the electrical quantity related to the non-zero injection node.

[0066] From the above formula (1), we can get:

[0067]

[0068] Transforming equation (2) into a real number equation in rectangular coordinates, we can obtain:

[0069]

[0070] Among them, G is the real part of the power system admittance matrix, B is the imaginary part of the power system admittance matrix, e and f are the real and imaginary parts of the node complex voltage respectively.

[0071] Next, let x be the power system state variable, where x B =[e B f B ] T ,x N =[e N f N ] T You can get x B 、x N The expression between the two:

[0072] x B =Fx N (4)

[0073] Where F is the nonlinear mapping matrix.

[0074] in:

[0075]

[0076] Secondly, the embodiment of the present application can take into account the upper and lower limits of active output and reactive output of the photovoltaic power generation system in the distribution network, wherein the upper and lower limits of active output and reactive output of the photovoltaic power generation system are:

[0077]

[0078] Among them, P DG and Q DG Represents the actual active and reactive power output of photovoltaic, P DG,min and P DG,max Indicates the lower and upper limits of photovoltaic active output, Q DG,min and Q DG,max Indicates the lower and upper limits of PV reactive output.

[0079] In step S102, inequality constraints are determined according to the upper and lower limits of active power output and the upper and lower limits of reactive power output, and a distribution network state estimation model is established based on the inequality constraints.

[0080] It can be understood that the embodiment of the present application can determine the inequality constraints based on the upper and lower limits of active output and the upper and lower limits of reactive output, and introduce the corresponding inequality constraints in the initial power system state estimation model to establish a distribution network state estimation model. That is, the embodiment of the present application can add the upper and lower limits of active output and reactive output of the photovoltaic power generation system as inequality constraints, and transform the original unconstrained optimization problem into a constrained optimization problem, which can better reflect the actual situation, ensure that the operation of the photovoltaic power generation system is within an acceptable range, avoid adverse effects on the power grid, increase the scope of application of the initial power system state estimation model, and improve the accuracy and reliability of the distribution network state estimation.

[0081] Among them, in one embodiment of the present application, the distribution network state estimation model is:

[0082]

[0083] Among them, z i 、h i and w i are the measured values, measurement functions and weights of the voltage amplitude and phase angle of measurement i, respectively. are all power system state variables, J is the objective function, u is the voltage amplitude, and θ is the independent variable voltage phase angle.

[0084] In step S103, the distribution network state estimation model is optimized and solved using a preset trust region algorithm to obtain a target solution, so as to determine the distribution network state estimation result of the power system using the target solution.

[0085] In the embodiment of the present application, the target solution is the optimal solution obtained after optimization.

[0086] It can be understood that the embodiment of the present application can use the trust region algorithm in the following steps to optimize and solve the distribution network state estimation model to obtain the optimal solution, so as to use the optimal solution to determine the distribution network state estimation result of the power system, wherein the trust region algorithm can be applied to both rectangular coordinates and polar coordinates, with short iteration time and small memory usage, which effectively improves the efficiency and accuracy of the distribution network state estimation of the power system.

[0087] Among them, in one embodiment of the present application, a preset trust region algorithm is used to optimize and solve the distribution network state estimation model to obtain a target solution, including: determining the optimization problem of the distribution network state estimation model, and preprocessing the distribution network state estimation model to obtain a state estimation model that meets preset processing conditions; based on the state estimation model, converting the optimization problem into a sub-problem of quadratic programming, and solving the sub-problem within the trust region radius to obtain a solution result, and detecting whether the solution result meets a preset no-solution condition; when it is detected that the solution result meets the preset no-solution condition, performing feasibility recovery on the solution result to obtain a target solution result, and judging whether the target solution result meets the preset convergence condition; if the target solution result meets the preset convergence condition, updating the target variable value of the state estimation model, and using the target variable value to obtain the target solution, otherwise adjusting the trust region radius until the target solution result meets the preset convergence condition.

[0088] In the embodiment of the present application, the optimization solution using the trust region algorithm is mainly divided into four modules, namely, a sub-problem solving module, a feasibility implementation module, a module for judging whether the test step is acceptable, and a processing after importing the rejected test step, which will be explained in detail below.

[0089] For example, the embodiment of the present application can pre-process the distribution network state estimation model. By adding slack variables to the inequality constraints, the original inequality constraint problem can be converted into an equality constraint problem, which is convenient for the direct application of the following optimization algorithm, that is, the standard form is:

[0090]

[0091] Among them, f(x) is the objective function, and c(x) is the equality constraint adjusted from the equality constraint and the inequality constraint.

[0092] Next, the embodiment of the present application can use the trust region method for optimization and solution, that is, firstly transform the original optimization problem into a QP (Quadratic Programming) sub-problem and solve it within the trust region radius, wherein the expression of the trust region algorithm is:

[0093]

[0094] Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, the superscript k indicates the kth iteration; d indicates the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; λ (k) represents the Lagrange multiplier in the kth iteration; c(x (k) ) represents the value of the equation constraint at the kth iteration, where

[0095] Represents the Hessian matrix.

[0096] In one embodiment of the present application, performing feasibility recovery on the solution result to obtain the target solution result includes: based on a preset relaxation method, relaxing the constraints in the optimization problem to obtain the target solution result.

[0097] For example, if x in the original optimization problem of the subproblem solving module (k) If the constraints are not met, the QP subproblem will have no solution. In this case, the QP subproblem can be further processed by relaxing the constraints in the original optimization problem in the feasibility recovery module and using l 1 The relaxation method of relaxation, where the expression of the relaxation method is:

[0098]

[0099] Obtain the modified value d of the variable and relaxation factor for each step. The Armijo condition is used in the judgment of the test step. If:

[0100] Δmerit≥σΔm (11)

[0101] Wherein, σ represents a coefficient used to measure the degree of decline. In the embodiment of the present application, σ=10 -4 , Δmerit represents the actual reduction value. At this time, it can be determined that the sufficient reduction condition is met. Δm represents the predicted reduction value, which is the value before iteration minus the value updated in the test step. Specifically, it is expressed as:

[0102]

[0103] Among them, α is the step size factor, and its value is 1;

[0104] The actual reduction value at this time can be expressed as:

[0105]

[0106] Among them, ω is the norm of the equation constraint in the k-th iteration, and η is the value of the objective function in the k-th iteration.

[0107] Again, the embodiment of the present application can determine whether this test step is acceptable by judging the relationship between the actual reduction value and the predicted reduction value. If the sufficient reduction condition is met, the iteration step length is accepted, then: (k+1) =x (k) +αd,f(x (k+1) )=f(x (k) +αd).

[0108] Among them, in one embodiment of the present application, the trust region radius is adjusted until the target solution result meets the preset convergence condition, including: reducing the trust region radius to the target radius that meets the preset convergence condition, and adding a target constraint that meets the preset convergence condition in the sub-problem to convert the sub-problem into a target sub-problem; based on the target sub-problem, a new target solution result is obtained, so that the new target solution result meets the preset convergence condition.

[0109] For example, in the embodiment of the present application, in the processing module after the rejection test step is introduced, when the sufficient descent condition is not met, the test step is rejected. At this time, the radius of the trust region should be reduced, that is, Δ should be reduced. In the subsequent iterations, the constraints are added to the QP sub-problem:

[0110] ||d|| ∞ ≤Δ (14)

[0111] Among them, Δ is the trust region radius, and its initial value is 10.

[0112] At this point, the QP subproblem is transformed into the target subproblem to be solved again, and the above steps are repeated, where the expression of the target subproblem is:

[0113]

[0114] Regarding the final convergence criterion, it can be set by relevant technical personnel, for example:

[0115] f(x (k) )-f(x (k+1) )≤10 -4 (16)

[0116] In the embodiment of the present application, if the difference between two consecutive updates of the objective function is less than 10-4 , it is considered to have converged and satisfied the criterion, otherwise it needs to continue iterating.

[0117] For example, Figure 2 As shown, the working principle of optimizing and solving the trust region algorithm is described in detail below with a specific embodiment.

[0118] Step S201: data initialization, that is, initializing the power system state variables and the applied slack variables to pave the way for subsequent iterations, wherein the initial values ​​of the power system state variables are per-unit values, and the initial values ​​of the slack variables are 0;

[0119] Step S202: determine whether the initialized data meets the convergence standard. If so, execute step S203 to exit the iteration. If not, execute step S204. In the initialization phase, calculation of this step does not meet the convergence standard by default.

[0120] Step S203: exit iteration and complete the optimization solution.

[0121] Step S204: solving the QP sub-problem, that is, converting the original optimization problem into a QP sub-problem and solving it within the trust region radius.

[0122] Step S205: Determine whether the optimization solution is feasible. If it is feasible, execute step S207; otherwise, execute step S206.

[0123] Step S206: Feasibility recovery, that is, if the state variables in the original optimization problem do not satisfy the constraints, the QP subproblem will be unsolvable. At this time, the QP subproblem is further constrained and l 1 The relaxation method relaxes the constraints in the original optimization problem in the feasibility recovery module, and after the optimization solution is completed, it goes to step S207.

[0124] Step S207: Determine whether the solution meets the Armijo condition. If the Armijo condition is met, execute step S210; otherwise, execute step S208.

[0125] Step S208: adjusting the trust region radius, that is, reducing the trust region radius.

[0126] Step S209: adding constraints to the original QP sub-problem. After adding the corresponding constraints, execute step S202.

[0127] Step S210: Update the variable value. After the variable is updated, execute step S202.

[0128] According to the distribution network state estimation method based on trust region proposed in the embodiment of the present application, the inequality constraint conditions can be determined according to the upper and lower limits of the active output and the upper and lower limits of the reactive output of the photovoltaic power generation system in the distribution network of the power system, and based on the inequality constraint conditions, a distribution network state estimation model is established, and the distribution network state estimation model is optimized and solved using the trust region algorithm to obtain the target solution, so as to determine the distribution network state estimation result of the power system using the target solution, effectively improving the applicability and efficiency of the power system state estimation. As a result, the problems of long algorithm iteration time, large memory usage, poor applicability, and reduced applicability and efficiency of power system state estimation in the related technology are solved.

[0129] Next, a distribution network state estimation device based on a trust region proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0130] Figure 3 It is a block diagram of a distribution network state estimation device based on a trust region according to an embodiment of the present application.

[0131] like Figure 3 As shown, the trust region-based distribution network state estimation device 10 includes: an acquisition module 100 , an establishment module 200 and a determination module 300 .

[0132] Specifically, the acquisition module 100 is used to acquire the upper and lower limits of active output and reactive output of the photovoltaic power generation system in the power distribution network of the power system.

[0133] The module 200 is established to determine inequality constraints according to the upper and lower limits of active power output and the upper and lower limits of reactive power output, and to establish a distribution network state estimation model based on the inequality constraints.

[0134] The determination module 300 is used to optimize and solve the distribution network state estimation model using a preset trust region algorithm to obtain a target solution, so as to determine the distribution network state estimation result of the power system using the target solution.

[0135] Optionally, in one embodiment of the present application, the determination module 300 includes: a determination unit, a detection unit, a judgment unit and a processing unit.

[0136] The determination unit is used to determine the optimization problem of the distribution network state estimation model, and preprocess the distribution network state estimation model to obtain a state estimation model that meets preset processing conditions.

[0137] The detection unit is used to transform the optimization problem into a sub-problem of quadratic programming based on the state estimation model, solve the sub-problem within the trust region radius, obtain the solution result, and detect whether the solution result meets the preset no-solution condition.

[0138] The judgment unit is used to perform feasibility recovery on the solution result when it is detected that the solution result meets the preset no-solution condition, obtain the target solution result, and judge whether the target solution result meets the preset convergence condition.

[0139] The processing unit is used to update the target variable value of the state estimation model and obtain the target solution using the target variable value if the target solution result meets the preset convergence condition, otherwise adjust the trust region radius until the target solution result meets the preset convergence condition.

[0140] Optionally, in one embodiment of the present application, the judgment unit is further used to relax the constraints in the optimization problem based on a preset relaxation method to obtain a target solution result.

[0141] Optionally, in one embodiment of the present application, the expression of the preset relaxation method is:

[0142]

[0143] Where d represents the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; c(x (k) ) represents the value of the equation constraint in the kth iteration; x (k) Represents the value of the independent variable at the kth iteration.

[0144] Optionally, in one embodiment of the present application, the processing unit includes: a processing subunit and a determining subunit.

[0145] The processing subunit is used to reduce the trust region radius to a target radius that satisfies a preset convergence condition, and to add a target constraint that satisfies the preset convergence condition to the subproblem, so as to transform the subproblem into a target subproblem.

[0146] The subunit is determined to obtain a new target solution result based on the target subproblem, so that the new target solution result meets the preset convergence condition.

[0147] Optionally, in one embodiment of the present application, the expression of the preset convergence condition is:

[0148] Δmerit ≥ σΔm

[0149] Among them, σ represents a coefficient used to measure the degree of decline, Δmerit represents the actual reduction value, and Δm represents the predicted decline value.

[0150] Optionally, in one embodiment of the present application, the expression of the target sub-problem is:

[0151]

[0152] Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, and the superscript k indicates the kth iteration; λ (k) represents the Lagrange multiplier in the kth iteration.

[0153] Optionally, in one embodiment of the present application, the upper and lower limits of active output and reactive output of the photovoltaic power generation system are:

[0154]

[0155] Among them, P DG and Q DG Represents the actual active and reactive power output of photovoltaic, P DG,min and P DG,max Indicates the lower and upper limits of photovoltaic active output, Q DG,min and Q DG,max Indicates the lower and upper limits of PV reactive output.

[0156] Optionally, in one embodiment of the present application, the distribution network state estimation model is:

[0157]

[0158] Among them, z i 、h i and w i are the measured values, measurement functions and weights of the voltage amplitude and phase angle of measurement i, respectively. are all system state variables, F is the nonlinear mapping matrix, u is the independent variable voltage amplitude, and θ is the independent variable voltage phase angle.

[0159] Optionally, in one embodiment of the present application, the expression of the preset trust region algorithm is:

[0160]

[0161] Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, the superscript k indicates the kth iteration; d indicates the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; λ (k) represents the Lagrange multiplier in the kth iteration; c(x (k) ) represents the value of the equation constraint at the kth iteration, where Represents the Hessian matrix.

[0162] It should be noted that the aforementioned explanation of the embodiment of the distribution network state estimation method based on the trust region is also applicable to the distribution network state estimation device based on the trust region of this embodiment, and will not be repeated here.

[0163] According to the distribution network state estimation device based on trust region proposed in the embodiment of the present application, the inequality constraint conditions can be determined according to the upper and lower limits of the active output and the upper and lower limits of the reactive output of the photovoltaic power generation system in the distribution network of the power system, and based on the inequality constraint conditions, a distribution network state estimation model is established, and the distribution network state estimation model is optimized and solved using the trust region algorithm to obtain the target solution, so as to determine the distribution network state estimation result of the power system using the target solution, thereby effectively improving the applicability and efficiency of the power system state estimation. Thus, the problems of long algorithm iteration time, large memory usage, poor applicability, and reduced applicability and efficiency of power system state estimation in the related technology are solved.

[0164] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0165] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0166] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0167] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0168] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0169] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0170] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0171] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A distribution network state estimation method based on trust region, characterized in that: The following steps are involved: Obtaining the upper and lower limits of active output and reactive output of the photovoltaic power generation system in the distribution network of the power system; Determine inequality constraints according to the active power upper and lower limits and the reactive power upper and lower limits, and establish a distribution network state estimation model based on the inequality constraints; The distribution network state estimation model is optimized and solved using a preset trust region algorithm to obtain a target solution, so as to determine the distribution network state estimation result of the power system using the target solution.

2. The method according to claim 1, characterized in that The method of optimizing and solving the distribution network state estimation model by using a preset trust region algorithm to obtain a target solution includes: Determine the optimization problem of the distribution network state estimation model, and preprocess the distribution network state estimation model to obtain a state estimation model that meets preset processing conditions; Based on the state estimation model, the optimization problem is converted into a sub-problem of quadratic programming, and the sub-problem is solved within the trust region radius to obtain a solution result, and whether the solution result meets a preset no-solution condition is detected; When it is detected that the solution result satisfies the preset no-solution condition, performing feasibility recovery on the solution result to obtain a target solution result, and judging whether the target solution result satisfies a preset convergence condition; If the target solution result meets the preset convergence condition, the target variable value of the state estimation model is updated, and the target solution is obtained using the target variable value; otherwise, the trust region radius is adjusted until the target solution result meets the preset convergence condition.

3. The method according to claim 2, characterized in that The performing feasibility recovery on the solution result to obtain the target solution result comprises: Based on a preset relaxation method, the constraints in the optimization problem are relaxed to obtain the target solution.

4. The method according to claim 3, characterized in that: The expression of the preset relaxation method is: s.t.x (k) +d≥0 Where d represents the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; c(x (k) ) represents the value of the equation constraint in the kth iteration; x (k) Represents the value of the independent variable at the kth iteration.

5. The method according to claim 2, characterized in that: The adjusting the trust region radius until the target solution satisfies the preset convergence condition includes: The trust region radius is reduced to a target radius that satisfies a preset convergence condition, and a target constraint that satisfies the preset convergence condition is added to the subproblem to transform the subproblem into a target subproblem; Based on the target subproblem, a new target solution result is obtained, so that the new target solution result satisfies the preset convergence condition.

6. The method according to claim 5, characterized in that The expression of the preset convergence condition is: Δmerit ≥ σΔm Among them, σ represents a coefficient used to measure the degree of decline, Δmerit represents the actual reduction value, and Δm represents the predicted decline value.

7. The method according to claim 5, characterized in that The expression of the target subproblem is: x (k) +d≥0 ||d|| ∞ ≤Δ Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, and the superscript k indicates the kth iteration; λ (k) represents the Lagrange multiplier in the kth iteration.

8. The method according to claim 1, characterized in that The upper and lower limits of active output and reactive output of the photovoltaic power generation system are: Among them, P DG and Q DG Represents the actual active and reactive power output of photovoltaic, P DG,min and P DG,max Indicates the lower and upper limits of photovoltaic active output, Q DG,min and Q DG,max Indicates the lower and upper limits of PV reactive output.

9. The method according to claim 1, characterized in that: The distribution network state estimation model is: Among them, z i 、h i and w i are the measured values, measurement functions and weights of the voltage amplitude and phase angle of measurement i, respectively. are all system state variables, F is the nonlinear mapping matrix, u is the independent variable voltage amplitude, and θ is the independent variable voltage phase angle.

10. The method according to claim 1, characterized in that The expression of the preset trust region algorithm is: x (k) +d≥0 Among them, ρ represents the multiplication factor, which is generally 1; Indicates the gradient of the objective function, the superscript k indicates the kth iteration; d indicates the modified value of the variable and relaxation factor at each step; represents the Hamiltonian operator; λ (k) represents the Lagrange multiplier in the kth iteration; c(x (k) ) represents the value of the equation constraint at the kth iteration, where Represents the Hessian matrix.

11. A distribution network state estimation device based on trust region, characterized in that: include: An acquisition module, used to acquire the upper and lower limits of active output and reactive output of a photovoltaic power generation system in a distribution network of the power system; An establishment module is used to determine inequality constraints according to the active power output upper and lower limits and the reactive power output upper and lower limits, and establish a distribution network state estimation model based on the inequality constraints; The determination module is used to optimize and solve the distribution network state estimation model using a preset trust region algorithm to obtain a target solution, so as to determine the distribution network state estimation result of the power system using the target solution.

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